Spring, 2019

Monday and Wednesday 8:30AM-10:20AM

Friend Center 101

Wednesdays, 2:30PM-3:20PM; Thursdays, 10:00AM-10:50AM; Fridays, 3:30PM-4:20PM

Computer Science 105

Barbara Engelhardt

Office: Computer Science 322

Email: bee@princeton.edu

Hours: Monday 10:00-11:00AM; COS 302

Diana Cai, Jonathan Lu, Guillaume Martinet, Matthew Meyers, Archit Verma, Tianju Xue.

For TA office hours and locations, see Piazza website.

- Python coding and machine learning:
- sci-kit learn includes many python packages for a large range of machine learning methods and models.
- The iPython notebook is a simple data analysis tool for working with data in a reproducible way.

- Here are some resources for learning and using R if you care to use R in visualization (project code is expected to be in Python):
- Download R at the R Project for Statistical Computing.
- Start to learn R by reading Introductory Statistics with R by Peter Dalgaard (Ch 1-2).
- Many people like R studio.
- Some people use Emacs Speaks Statistics.
- Consider using ggplot2 for beautiful graphics and figures.
- Consider using KNITR for reproducible R pipelines.

- Additional books and reading that you might find useful (Murphy book PDF link is on the Piazza page):
- The Hastie et al. book,
*Elements of Statistical Learning*can be found here. - Michael Lavine, Introduction to Statistical Thought (an introductory statistical textbook with plenty of R examples, and it's online too)
- David J.C. MacKay Information Theory, Inference, and Learning Algorithms (PDF available online)
- Chris Bishop, Pattern Recognition and Machine Learning
- Daphne Koller & Nir Friedman, Probabilistic Graphical Models

- The Hastie et al. book,